We propose a new layer called the pseudo morphological gradient. For this layer, we achieve the same Projection convolution step, and Pixel Shuffle step. Then we get pseudo morphological dilation feature maps using max-pooling, and we get the final gradient feature map by performing vector subtraction with the input image. Contrary to the morphological gradient which is positive for all pixels, this one is not necessarily positive. This is also happening with the morphological Laplacian [1]} that is interesting to denoise images. Also, the skip connection provided by this gradient layer can help to avoid vanishing gradients. This layer is illustrated in Figure REF .
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